About
Hello, I'm John Winder. I research artificial intelligence (AI) and machine learning for complex real-world systems. I co-lead a technical organization of over 50 scientists and engineers who design, develop, test, and evaluate AI and autonomy for intelligent systems at the Johns Hopkins University Applied Physics Laboratory (JHU/APL). I also serve as a Faculty Member of the Data Science and AI Institute (DSAI) at JHU.
I received my Ph.D. in Computer Science from UMBC, where I specialized in reinforcement learning (RL). I focused on developing state abstractions for hierarchical RL and probabilistic planning. I had two excellent doctoral advisors, Marie desJardins and Cynthia Matuszek.
My main interest and objective is the creation of decision-making agents that generalize and reason about long-term goals under uncertainty, collaborating with humans and other AI agents, while operating in dynamic and open environments, facing new challenges in diverse scenarios.
News
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October 2025I am now serving as the Chief AI Officer for the Force Projection Sector at APL, setting and executing strategy for AI, accelerating its adoption across the workforce, and increasing the positive impact of AI for national security.
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March 2025Recently I was promoted to Assistant Group Supervisor of the Intelligent Systems Group at APL. We are hiring!
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October 2024The NeurIPS 2024 Workshop on Foundation Model Interventions has accepted our paper on the Iterative Inference Hypothesis for uncovering uncertainty in transformers.
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October 2024Congratulations to Josh McClellan on the acceptance of his first authored paper at the NeurIPS 2024 main conference for our research in equivariance for multi-agent RL!
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September 2024I have been appointed as a Faculty Member of the Data Science and Artificial Intelligence Institute (DSAI) at Johns Hopkins University. If you are at JHU and would like to collaborate on AI research, please reach out!
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August 2024I was invited to give a talk to the Institute for Assured Autonomy on my team's research into human-machine teaming for AI fighter pilots.
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June 2024Our conference paper on aligning the latent space of variational autoencoders with human behaviors, "Generative Artificial Intelligence for Behavioral Intent Prediction" was recently accepted at CogSci 2024!
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May 2024A huge milestone for the whole ACE team, yesterday the Secretary of the Air Force Frank Kendall flew on the AI-controlled X-62A VISTA. Bravo Zulu to the APL team for making this a reality!
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February 2024The entire DARPA ACE team is being recognized as a finalist for the 2023 Robert J. Collier Trophy, including the contributions of my team at APL towards advancing intelligent autonomy for aircraft.
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July 2023I gave a talk on Beyond Human Reasoning, my team's multi-year effort to develop an AI co-pilot, at APL's 2023 XR Symposium.
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November 2022I just finished meta-reviews for AAAI 2023. This time was my first serving in the senior program committee, excited to see the pace of research continue to pick up.
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August 2022Congrats to Adam Berlier and the IRAL Lab on their paper "Augmenting Simulation Data with Sensor Effects for Improved Domain Transfer" being accepted at the European Conference on Computer Vision (ECCV) Workshop on Assistive Computer Vision and Robotics (ACVR).
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July 2022APL colleague Josh Bertram has successfully defended his dissertation on FastMDP, an extremely efficient solution for complex multi-agent planning problems. Congratulations, Dr. Bertram!
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August 2021Our paper on the GoLD dataset for grounded learning of spoken language descriptions has been accepted at NeurIPS 2021. Congrats to the IRAL lab!
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February 2021I've been promoted to Section Supervisor for the Advanced Artificial Intelligence Algorithms section at APL, working on RL and autonomous systems. We're hiring!
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July 2020I joined APL as a Senior Professional Staff Scientist researching RL for intelligent platforms.
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July 2020Our National Robotics Initiative proposal was awarded a three year grant! The IRAL lab will be studying grounded language learning and concept-based knowledge transfer in deep RL for collaborative robots.
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November 2019Our paper on abstract model-based RL has been accepted to AAAI 2020. See you there in February!
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October 2019I'm presenting our research on learning abstract models at the Do Good Robotics Symposium, discussing how our work can be used in domestic service robots.
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September 2019I've been hired as a faculty research assistant at UMBC in the Interactive Robotics and Language Lab (IRAL), advising student research groups working in human-robot interaction and concept formation for RL.
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June 2019I successfully defended my dissertation and passed my final exam! I'll be graduating in August.
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May 2019My paper The Expected-Length Model of Options has been accepted at IJCAI-19, joint work with Dave Abel and our advisors.
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August 2018Our NSF IIS proposal on concept formation in POMDPs was accepted.
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August 2018I'm joining the IRAL lab at UMBC.
Selected Publications
- Joshua McClellan, Naveed Haghani, John Winder, Furong Huang, Pratap Tokekar. Boosting Sample Efficiency and Generalization in Multi-agent Reinforcement Learning via Equivariance. Proceedings of The Thirty-eighth Conference on Neural Information Processing Systems (NeurIPS 2024). 2024.
- John Winder, Stephanie Milani, Matthew Landen, Erebus Oh, Shane Parr, Shawn Squire, Marie desJardins, and Cynthia Matuszek. Planning with Abstract Learned Models While Learning Transferable Subtasks. Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20). 2020.
- David Abel*, John Winder*, Marie desJardins, Michael L. Littman. The Expected-Length Model of Options. Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI-19) [*equal contribution]. 2019.
- John Winder, Marie desJardins. Concept-Aware Feature Extraction for Knowledge Transfer in Reinforcement Learning. Knowledge Extraction from Games (KEG-18) Workshop at the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18). 2018.
- Karan K Budhraja, John Winder, Tim Oates. Feature Construction for Controlling Swarms by Visual Demonstrations. ACM Transactions on Autonomous and Adaptive Systems (TAAS), 12(2), 10. 2017.
- John Winder, Shawn Squire, Matthew Landen, Stephanie Milani, Marie desJardins. Towards Planning With Hierarchies of Learned Markov Decision Processes. Integrated Execution of Planning and Acting Workshop (IntEx-17) at the Twenty-Seventh International Conference on Automated Planning and Scheduling (ICAPS-17). 2017.
- Nakul Gopalan, Marie desJardins, Michael L Littman, James MacGlashan, Shawn Squire, Stefanie Tellex, John Winder, Lawson LS Wong. Planning with Abstract Markov Decision Processes. Proceedings of the Twenty-Seventh International Conference on Automated Planning and Scheduling (ICAPS-17). 2017.
- Nicholay Topin, Nicholas Haltmeyer, Shawn Squire, John Winder, Marie desJardins, James MacGlashan. Portable Option Discovery for Automated Learning Transfer in Object-Oriented Markov Decision Processes. Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI-15). 2015.